Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations19130
Missing cells120
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory192.0 B

Variable types

Categorical2
DateTime4
Text6
Numeric11
Boolean1

Alerts

actual_distance_to_destination is highly overall correlated with actual_time and 4 other fieldsHigh correlation
actual_time is highly overall correlated with actual_distance_to_destination and 4 other fieldsHigh correlation
cutoff_factor is highly overall correlated with actual_distance_to_destination and 4 other fieldsHigh correlation
factor is highly overall correlated with segment_factorHigh correlation
osrm_distance is highly overall correlated with actual_distance_to_destination and 5 other fieldsHigh correlation
osrm_time is highly overall correlated with actual_distance_to_destination and 5 other fieldsHigh correlation
route_type is highly overall correlated with start_scan_to_end_scanHigh correlation
segment_actual_time is highly overall correlated with segment_osrm_distance and 1 other fieldsHigh correlation
segment_factor is highly overall correlated with factorHigh correlation
segment_osrm_distance is highly overall correlated with osrm_distance and 4 other fieldsHigh correlation
segment_osrm_time is highly overall correlated with segment_actual_time and 1 other fieldsHigh correlation
start_scan_to_end_scan is highly overall correlated with actual_distance_to_destination and 6 other fieldsHigh correlation
factor is highly skewed (γ1 = 20.41257294) Skewed
segment_factor is highly skewed (γ1 = 62.26346765) Skewed
segment_actual_time has 253 (1.3%) zeros Zeros
segment_osrm_time has 311 (1.6%) zeros Zeros
segment_osrm_distance has 207 (1.1%) zeros Zeros

Reproduction

Analysis started2024-10-30 11:16:27.767197
Analysis finished2024-10-30 11:17:13.775755
Duration46.01 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

data
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
training
13822 
test
5308 

Length

Max length8
Median length8
Mean length6.8901202
Min length4

Characters and Unicode

Total characters131808
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtraining
2nd rowtraining
3rd rowtraining
4th rowtraining
5th rowtraining

Common Values

ValueCountFrequency (%)
training 13822
72.3%
test 5308
 
27.7%

Length

2024-10-30T11:17:13.945423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T11:17:14.254691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
training 13822
72.3%
test 5308
 
27.7%

Most occurring characters

ValueCountFrequency (%)
i 27644
21.0%
n 27644
21.0%
t 24438
18.5%
r 13822
10.5%
a 13822
10.5%
g 13822
10.5%
e 5308
 
4.0%
s 5308
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 27644
21.0%
n 27644
21.0%
t 24438
18.5%
r 13822
10.5%
a 13822
10.5%
g 13822
10.5%
e 5308
 
4.0%
s 5308
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 27644
21.0%
n 27644
21.0%
t 24438
18.5%
r 13822
10.5%
a 13822
10.5%
g 13822
10.5%
e 5308
 
4.0%
s 5308
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 27644
21.0%
n 27644
21.0%
t 24438
18.5%
r 13822
10.5%
a 13822
10.5%
g 13822
10.5%
e 5308
 
4.0%
s 5308
 
4.0%
Distinct2013
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
Minimum2018-09-12 00:25:19.499696
Maximum2018-10-03 23:59:42.701692
2024-10-30T11:17:14.513576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:14.826252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct962
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:15.283395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length51
Median length51
Mean length51
Min length51

Characters and Unicode

Total characters975630
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowthanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297ef
2nd rowthanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297ef
3rd rowthanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297ef
4th rowthanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297ef
5th rowthanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297ef
ValueCountFrequency (%)
thanos::sroute:e2f5faaa-455a-494b-a501-549c3e3081ec 278
 
1.5%
thanos::sroute:dca6268f-741a-4d1a-b1b0-aab13095a366 231
 
1.2%
thanos::sroute:0456b740-1dad-4929-bbe0-87d8843f5a10 231
 
1.2%
thanos::sroute:a4bf93af-8105-4dff-818e-cb79ddd69761 224
 
1.2%
thanos::sroute:366da0f3-1979-4793-973f-27da63568435 208
 
1.1%
thanos::sroute:162f9a67-5ebe-4338-8450-1c6d57d2c7b1 185
 
1.0%
thanos::sroute:fa83fd49-3327-4503-8e80-bf58ed636cc7 184
 
1.0%
thanos::sroute:67c77992-49e3-4594-9a75-9861ef082a00 183
 
1.0%
thanos::sroute:be1c03eb-fd2f-4455-a933-5e3d0857e027 179
 
0.9%
thanos::sroute:15c921f8-8a40-44cc-95ac-59f93d25f780 172
 
0.9%
Other values (952) 17055
89.2%
2024-10-30T11:17:16.000637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 76520
 
7.8%
a 63337
 
6.5%
: 57390
 
5.9%
e 55231
 
5.7%
4 54618
 
5.6%
9 41209
 
4.2%
8 39098
 
4.0%
b 39007
 
4.0%
5 38356
 
3.9%
t 38260
 
3.9%
Other values (15) 472604
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 975630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 76520
 
7.8%
a 63337
 
6.5%
: 57390
 
5.9%
e 55231
 
5.7%
4 54618
 
5.6%
9 41209
 
4.2%
8 39098
 
4.0%
b 39007
 
4.0%
5 38356
 
3.9%
t 38260
 
3.9%
Other values (15) 472604
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 975630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 76520
 
7.8%
a 63337
 
6.5%
: 57390
 
5.9%
e 55231
 
5.7%
4 54618
 
5.6%
9 41209
 
4.2%
8 39098
 
4.0%
b 39007
 
4.0%
5 38356
 
3.9%
t 38260
 
3.9%
Other values (15) 472604
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 975630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 76520
 
7.8%
a 63337
 
6.5%
: 57390
 
5.9%
e 55231
 
5.7%
4 54618
 
5.6%
9 41209
 
4.2%
8 39098
 
4.0%
b 39007
 
4.0%
5 38356
 
3.9%
t 38260
 
3.9%
Other values (15) 472604
48.4%

route_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
FTL
13117 
Carting
6013 

Length

Max length7
Median length3
Mean length4.2572922
Min length3

Characters and Unicode

Total characters81442
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarting
2nd rowCarting
3rd rowCarting
4th rowCarting
5th rowCarting

Common Values

ValueCountFrequency (%)
FTL 13117
68.6%
Carting 6013
31.4%

Length

2024-10-30T11:17:16.319486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T11:17:16.583000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ftl 13117
68.6%
carting 6013
31.4%

Most occurring characters

ValueCountFrequency (%)
F 13117
16.1%
T 13117
16.1%
L 13117
16.1%
C 6013
7.4%
a 6013
7.4%
r 6013
7.4%
t 6013
7.4%
i 6013
7.4%
n 6013
7.4%
g 6013
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 13117
16.1%
T 13117
16.1%
L 13117
16.1%
C 6013
7.4%
a 6013
7.4%
r 6013
7.4%
t 6013
7.4%
i 6013
7.4%
n 6013
7.4%
g 6013
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 13117
16.1%
T 13117
16.1%
L 13117
16.1%
C 6013
7.4%
a 6013
7.4%
r 6013
7.4%
t 6013
7.4%
i 6013
7.4%
n 6013
7.4%
g 6013
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 13117
16.1%
T 13117
16.1%
L 13117
16.1%
C 6013
7.4%
a 6013
7.4%
r 6013
7.4%
t 6013
7.4%
i 6013
7.4%
n 6013
7.4%
g 6013
7.4%
Distinct2013
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:16.934614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length23
Mean length23.000366
Min length23

Characters and Unicode

Total characters439997
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)0.3%

Sample

1st rowtrip-153741093647649320
2nd rowtrip-153741093647649320
3rd rowtrip-153741093647649320
4th rowtrip-153741093647649320
5th rowtrip-153741093647649320
ValueCountFrequency (%)
trip-153811219535896559 101
 
0.5%
trip-153854253003897121 79
 
0.4%
trip-153828994104597545 78
 
0.4%
trip-153796781323528185 78
 
0.4%
trip-153716182991453455 77
 
0.4%
trip-153841720103113533 77
 
0.4%
trip-153738046125722247 77
 
0.4%
trip-153716392193219844 77
 
0.4%
trip-153733496685379517 77
 
0.4%
trip-153846880073894675 77
 
0.4%
Other values (2003) 18332
95.8%
2024-10-30T11:17:17.583394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 47087
10.7%
1 47006
10.7%
3 45553
10.4%
7 37077
 
8.4%
8 31983
 
7.3%
6 28971
 
6.6%
9 27429
 
6.2%
2 26494
 
6.0%
0 26489
 
6.0%
4 26258
 
6.0%
Other values (5) 95650
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 439997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 47087
10.7%
1 47006
10.7%
3 45553
10.4%
7 37077
 
8.4%
8 31983
 
7.3%
6 28971
 
6.6%
9 27429
 
6.2%
2 26494
 
6.0%
0 26489
 
6.0%
4 26258
 
6.0%
Other values (5) 95650
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 439997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 47087
10.7%
1 47006
10.7%
3 45553
10.4%
7 37077
 
8.4%
8 31983
 
7.3%
6 28971
 
6.6%
9 27429
 
6.2%
2 26494
 
6.0%
0 26489
 
6.0%
4 26258
 
6.0%
Other values (5) 95650
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 439997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 47087
10.7%
1 47006
10.7%
3 45553
10.4%
7 37077
 
8.4%
8 31983
 
7.3%
6 28971
 
6.6%
9 27429
 
6.2%
2 26494
 
6.0%
0 26489
 
6.0%
4 26258
 
6.0%
Other values (5) 95650
21.7%
Distinct1048
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:18.101455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.999007
Min length11

Characters and Unicode

Total characters229541
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)0.3%

Sample

1st rowIND388121AAA
2nd rowIND388121AAA
3rd rowIND388121AAA
4th rowIND388121AAA
5th rowIND388121AAA
ValueCountFrequency (%)
ind000000acb 3248
 
17.0%
ind562132aaa 1079
 
5.6%
ind421302aag 980
 
5.1%
ind411033aaa 588
 
3.1%
ind501359aae 488
 
2.6%
ind560099aab 316
 
1.7%
ind131028aab 291
 
1.5%
ind160002aac 280
 
1.5%
ind712311aaa 260
 
1.4%
ind600056aab 259
 
1.4%
Other values (1038) 11341
59.3%
2024-10-30T11:17:18.854392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 43029
18.7%
0 40768
17.8%
D 19447
8.5%
N 19144
8.3%
I 19131
8.3%
1 16320
 
7.1%
2 13213
 
5.8%
3 11097
 
4.8%
5 8438
 
3.7%
4 8250
 
3.6%
Other values (17) 30704
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 229541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 43029
18.7%
0 40768
17.8%
D 19447
8.5%
N 19144
8.3%
I 19131
8.3%
1 16320
 
7.1%
2 13213
 
5.8%
3 11097
 
4.8%
5 8438
 
3.7%
4 8250
 
3.6%
Other values (17) 30704
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 229541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 43029
18.7%
0 40768
17.8%
D 19447
8.5%
N 19144
8.3%
I 19131
8.3%
1 16320
 
7.1%
2 13213
 
5.8%
3 11097
 
4.8%
5 8438
 
3.7%
4 8250
 
3.6%
Other values (17) 30704
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 229541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 43029
18.7%
0 40768
17.8%
D 19447
8.5%
N 19144
8.3%
I 19131
8.3%
1 16320
 
7.1%
2 13213
 
5.8%
3 11097
 
4.8%
5 8438
 
3.7%
4 8250
 
3.6%
Other values (17) 30704
13.4%
Distinct1044
Distinct (%)5.5%
Missing61
Missing (%)0.3%
Memory size149.6 KiB
2024-10-30T11:17:19.320436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length41
Mean length29.900729
Min length8

Characters and Unicode

Total characters570177
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.3%

Sample

1st rowAnand_VUNagar_DC (Gujarat)
2nd rowAnand_VUNagar_DC (Gujarat)
3rd rowAnand_VUNagar_DC (Gujarat)
4th rowAnand_VUNagar_DC (Gujarat)
5th rowAnand_VUNagar_DC (Gujarat)
ValueCountFrequency (%)
haryana 3905
 
9.2%
gurgaon_bilaspur_hb 3248
 
7.6%
maharashtra 2654
 
6.2%
karnataka 2479
 
5.8%
pradesh 2276
 
5.3%
tamil 1093
 
2.6%
nadu 1093
 
2.6%
bangalore_nelmngla_h 1079
 
2.5%
bhiwandi_mankoli_hb 980
 
2.3%
telangana 900
 
2.1%
Other values (1085) 22889
53.7%
2024-10-30T11:17:20.336123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 93643
 
16.4%
r 41282
 
7.2%
_ 37291
 
6.5%
n 30665
 
5.4%
23527
 
4.1%
h 23048
 
4.0%
l 19642
 
3.4%
u 19246
 
3.4%
) 19068
 
3.3%
( 19068
 
3.3%
Other values (57) 243697
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 570177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 93643
 
16.4%
r 41282
 
7.2%
_ 37291
 
6.5%
n 30665
 
5.4%
23527
 
4.1%
h 23048
 
4.0%
l 19642
 
3.4%
u 19246
 
3.4%
) 19068
 
3.3%
( 19068
 
3.3%
Other values (57) 243697
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 570177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 93643
 
16.4%
r 41282
 
7.2%
_ 37291
 
6.5%
n 30665
 
5.4%
23527
 
4.1%
h 23048
 
4.0%
l 19642
 
3.4%
u 19246
 
3.4%
) 19068
 
3.3%
( 19068
 
3.3%
Other values (57) 243697
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 570177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 93643
 
16.4%
r 41282
 
7.2%
_ 37291
 
6.5%
n 30665
 
5.4%
23527
 
4.1%
h 23048
 
4.0%
l 19642
 
3.4%
u 19246
 
3.4%
) 19068
 
3.3%
( 19068
 
3.3%
Other values (57) 243697
42.7%
Distinct1036
Distinct (%)5.4%
Missing1
Missing (%)< 0.1%
Memory size149.6 KiB
2024-10-30T11:17:20.982517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.999164
Min length11

Characters and Unicode

Total characters229532
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.3%

Sample

1st rowIND388620AAB
2nd rowIND388620AAB
3rd rowIND388620AAB
4th rowIND388620AAB
5th rowIND388620AAB
ValueCountFrequency (%)
ind000000acb 1717
 
9.0%
ind562132aaa 1111
 
5.8%
ind421302aag 849
 
4.4%
ind501359aae 678
 
3.5%
ind411033aaa 626
 
3.3%
ind712311aaa 511
 
2.7%
ind131028aab 461
 
2.4%
ind160002aac 428
 
2.2%
ind751002aab 396
 
2.1%
ind382430aab 382
 
2.0%
Other values (1026) 11970
62.6%
2024-10-30T11:17:22.055461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 45454
19.8%
0 33497
14.6%
D 19407
8.5%
I 19129
8.3%
N 19129
8.3%
1 18527
8.1%
2 14945
 
6.5%
3 12383
 
5.4%
5 9616
 
4.2%
4 8093
 
3.5%
Other values (18) 29352
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 229532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 45454
19.8%
0 33497
14.6%
D 19407
8.5%
I 19129
8.3%
N 19129
8.3%
1 18527
8.1%
2 14945
 
6.5%
3 12383
 
5.4%
5 9616
 
4.2%
4 8093
 
3.5%
Other values (18) 29352
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 229532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 45454
19.8%
0 33497
14.6%
D 19407
8.5%
I 19129
8.3%
N 19129
8.3%
1 18527
8.1%
2 14945
 
6.5%
3 12383
 
5.4%
5 9616
 
4.2%
4 8093
 
3.5%
Other values (18) 29352
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 229532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 45454
19.8%
0 33497
14.6%
D 19407
8.5%
I 19129
8.3%
N 19129
8.3%
1 18527
8.1%
2 14945
 
6.5%
3 12383
 
5.4%
5 9616
 
4.2%
4 8093
 
3.5%
Other values (18) 29352
12.8%
Distinct1030
Distinct (%)5.4%
Missing43
Missing (%)0.2%
Memory size149.6 KiB
2024-10-30T11:17:22.680090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length40
Mean length30.038194
Min length13

Characters and Unicode

Total characters573339
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.3%

Sample

1st rowKhambhat_MotvdDPP_D (Gujarat)
2nd rowKhambhat_MotvdDPP_D (Gujarat)
3rd rowKhambhat_MotvdDPP_D (Gujarat)
4th rowKhambhat_MotvdDPP_D (Gujarat)
5th rowKhambhat_MotvdDPP_D (Gujarat)
ValueCountFrequency (%)
maharashtra 2653
 
6.1%
pradesh 2650
 
6.1%
karnataka 2617
 
6.0%
haryana 2575
 
5.9%
gurgaon_bilaspur_hb 1717
 
4.0%
tamil 1136
 
2.6%
nadu 1136
 
2.6%
bangalore_nelmngla_h 1111
 
2.6%
gujarat 1050
 
2.4%
telangana 1047
 
2.4%
Other values (1069) 25706
59.2%
2024-10-30T11:17:23.533799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 93038
 
16.2%
r 39479
 
6.9%
_ 37344
 
6.5%
n 30242
 
5.3%
h 24506
 
4.3%
24311
 
4.2%
) 19087
 
3.3%
( 19087
 
3.3%
l 19028
 
3.3%
e 18601
 
3.2%
Other values (56) 248616
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 573339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 93038
 
16.2%
r 39479
 
6.9%
_ 37344
 
6.5%
n 30242
 
5.3%
h 24506
 
4.3%
24311
 
4.2%
) 19087
 
3.3%
( 19087
 
3.3%
l 19028
 
3.3%
e 18601
 
3.2%
Other values (56) 248616
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 573339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 93038
 
16.2%
r 39479
 
6.9%
_ 37344
 
6.5%
n 30242
 
5.3%
h 24506
 
4.3%
24311
 
4.2%
) 19087
 
3.3%
( 19087
 
3.3%
l 19028
 
3.3%
e 18601
 
3.2%
Other values (56) 248616
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 573339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 93038
 
16.2%
r 39479
 
6.9%
_ 37344
 
6.5%
n 30242
 
5.3%
h 24506
 
4.3%
24311
 
4.2%
) 19087
 
3.3%
( 19087
 
3.3%
l 19028
 
3.3%
e 18601
 
3.2%
Other values (56) 248616
43.4%
Distinct3664
Distinct (%)19.2%
Missing1
Missing (%)< 0.1%
Memory size149.6 KiB
Minimum2018-09-12 00:39:30.747127
Maximum2018-10-05 08:35:15.664489
2024-10-30T11:17:23.846575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:24.161101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3664
Distinct (%)19.2%
Missing1
Missing (%)< 0.1%
Memory size149.6 KiB
Minimum2018-09-12 01:32:05.649177
Maximum2018-10-05 19:10:48.347039
2024-10-30T11:17:24.453182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:24.725284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

start_scan_to_end_scan
Real number (ℝ)

High correlation 

Distinct830
Distinct (%)4.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean869.03131
Minimum25
Maximum3560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:24.999950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile68
Q1149
median402
Q31352
95-th percentile3001
Maximum3560
Range3535
Interquartile range (IQR)1203

Descriptive statistics

Standard deviation962.42331
Coefficient of variation (CV)1.1074668
Kurtosis0.14039947
Mean869.03131
Median Absolute Deviation (MAD)303
Skewness1.210742
Sum16623700
Variance926258.63
MonotonicityNot monotonic
2024-10-30T11:17:25.311275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3341 135
 
0.7%
3178 113
 
0.6%
86 79
 
0.4%
3259 79
 
0.4%
3116 78
 
0.4%
2998 77
 
0.4%
3042 77
 
0.4%
136 77
 
0.4%
2874 77
 
0.4%
3001 77
 
0.4%
Other values (820) 18260
95.5%
ValueCountFrequency (%)
25 2
 
< 0.1%
29 5
 
< 0.1%
30 2
 
< 0.1%
31 2
 
< 0.1%
32 7
 
< 0.1%
33 3
 
< 0.1%
34 14
0.1%
35 16
0.1%
36 14
0.1%
37 19
0.1%
ValueCountFrequency (%)
3560 67
0.4%
3341 135
0.7%
3259 79
0.4%
3230 77
0.4%
3178 113
0.6%
3116 78
0.4%
3114 60
0.3%
3113 54
 
0.3%
3089 49
 
0.3%
3056 77
0.4%

is_cutoff
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size149.6 KiB
True
15502 
False
3627 
(Missing)
 
1
ValueCountFrequency (%)
True 15502
81.0%
False 3627
 
19.0%
(Missing) 1
 
< 0.1%
2024-10-30T11:17:25.606814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

cutoff_factor
Real number (ℝ)

High correlation 

Distinct367
Distinct (%)1.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean212.43128
Minimum9
Maximum1722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:25.851433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9
Q122
median54
Q3242
95-th percentile990
Maximum1722
Range1713
Interquartile range (IQR)220

Descriptive statistics

Standard deviation325.97709
Coefficient of variation (CV)1.534506
Kurtosis4.2369428
Mean212.43128
Median Absolute Deviation (MAD)37
Skewness2.1567782
Sum4063598
Variance106261.06
MonotonicityNot monotonic
2024-10-30T11:17:26.147917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 1893
 
9.9%
9 1648
 
8.6%
44 1201
 
6.3%
18 1120
 
5.9%
66 801
 
4.2%
27 763
 
4.0%
88 602
 
3.1%
110 495
 
2.6%
36 469
 
2.5%
132 428
 
2.2%
Other values (357) 9709
50.8%
ValueCountFrequency (%)
9 1648
8.6%
10 59
 
0.3%
11 59
 
0.3%
12 46
 
0.2%
13 46
 
0.2%
14 69
 
0.4%
15 78
 
0.4%
16 81
 
0.4%
17 61
 
0.3%
18 1120
5.9%
ValueCountFrequency (%)
1722 1
 
< 0.1%
1720 1
 
< 0.1%
1716 2
 
< 0.1%
1694 4
 
< 0.1%
1690 1
 
< 0.1%
1689 10
0.1%
1688 1
 
< 0.1%
1687 1
 
< 0.1%
1672 15
0.1%
1650 15
0.1%
Distinct17311
Distinct (%)90.5%
Missing1
Missing (%)< 0.1%
Memory size149.6 KiB
Minimum2018-09-12 00:47:25
Maximum2018-10-05 17:12:00
2024-10-30T11:17:26.442000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:26.742199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

actual_distance_to_destination
Real number (ℝ)

High correlation 

Distinct19087
Distinct (%)99.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean213.53329
Minimum9.0002674
Maximum1722.0098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:27.020508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.0002674
5-th percentile9.664379
Q123.086633
median55.362397
Q3242.66601
95-th percentile991.36818
Maximum1722.0098
Range1713.0095
Interquartile range (IQR)219.57938

Descriptive statistics

Standard deviation326.18006
Coefficient of variation (CV)1.5275373
Kurtosis4.2328485
Mean213.53329
Median Absolute Deviation (MAD)37.656755
Skewness2.1557363
Sum4084678.2
Variance106393.43
MonotonicityNot monotonic
2024-10-30T11:17:27.318311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.26259134 2
 
< 0.1%
89.22987309 2
 
< 0.1%
22.69777197 2
 
< 0.1%
40.57180786 2
 
< 0.1%
9.574436922 2
 
< 0.1%
28.9936929 2
 
< 0.1%
68.65791654 2
 
< 0.1%
22.98932082 2
 
< 0.1%
10.00743576 2
 
< 0.1%
37.24074081 2
 
< 0.1%
Other values (19077) 19109
99.9%
ValueCountFrequency (%)
9.000267384 1
< 0.1%
9.000543467 1
< 0.1%
9.000990629 1
< 0.1%
9.001577078 1
< 0.1%
9.001647607 1
< 0.1%
9.002115228 1
< 0.1%
9.002821678 1
< 0.1%
9.00359161 1
< 0.1%
9.004551406 1
< 0.1%
9.004579901 1
< 0.1%
ValueCountFrequency (%)
1722.009755 1
< 0.1%
1720.765247 1
< 0.1%
1717.23169 1
< 0.1%
1716.603188 1
< 0.1%
1695.72981 1
< 0.1%
1694.671809 1
< 0.1%
1694.370209 1
< 0.1%
1694.277774 1
< 0.1%
1690.302865 1
< 0.1%
1689.847168 1
< 0.1%

actual_time
Real number (ℝ)

High correlation 

Distinct2206
Distinct (%)11.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean377.0471
Minimum9
Maximum3276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:27.611924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile22
Q150
median119
Q3438
95-th percentile1709.6
Maximum3276
Range3267
Interquartile range (IQR)388

Descriptive statistics

Standard deviation552.51355
Coefficient of variation (CV)1.4653701
Kurtosis4.2758388
Mean377.0471
Median Absolute Deviation (MAD)87
Skewness2.1575545
Sum7212534
Variance305271.22
MonotonicityNot monotonic
2024-10-30T11:17:27.911854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 203
 
1.1%
38 196
 
1.0%
32 195
 
1.0%
42 192
 
1.0%
30 175
 
0.9%
26 167
 
0.9%
34 166
 
0.9%
39 154
 
0.8%
33 151
 
0.8%
50 143
 
0.7%
Other values (2196) 17387
90.9%
ValueCountFrequency (%)
9 13
 
0.1%
10 17
 
0.1%
11 36
 
0.2%
12 72
0.4%
13 50
 
0.3%
14 90
0.5%
15 92
0.5%
16 80
0.4%
17 85
0.4%
18 138
0.7%
ValueCountFrequency (%)
3276 1
< 0.1%
3219 1
< 0.1%
3180 1
< 0.1%
3162 1
< 0.1%
3102 1
< 0.1%
3066 1
< 0.1%
3033 1
< 0.1%
3031 1
< 0.1%
3003 1
< 0.1%
2973 1
< 0.1%

osrm_time
Real number (ℝ)

High correlation 

Distinct1277
Distinct (%)6.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean196.26886
Minimum6
Maximum1611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:28.223395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q126
median59
Q3220
95-th percentile888.6
Maximum1611
Range1605
Interquartile range (IQR)194

Descriptive statistics

Standard deviation292.91788
Coefficient of variation (CV)1.4924318
Kurtosis4.6959924
Mean196.26886
Median Absolute Deviation (MAD)41
Skewness2.2236541
Sum3754427
Variance85800.883
MonotonicityNot monotonic
2024-10-30T11:17:28.519806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 330
 
1.7%
21 330
 
1.7%
18 314
 
1.6%
22 311
 
1.6%
19 309
 
1.6%
17 282
 
1.5%
25 264
 
1.4%
16 259
 
1.4%
23 255
 
1.3%
24 255
 
1.3%
Other values (1267) 16220
84.8%
ValueCountFrequency (%)
6 24
 
0.1%
7 102
0.5%
8 133
0.7%
9 157
0.8%
10 168
0.9%
11 239
1.2%
12 226
1.2%
13 228
1.2%
14 221
1.2%
15 167
0.9%
ValueCountFrequency (%)
1611 1
 
< 0.1%
1610 1
 
< 0.1%
1605 1
 
< 0.1%
1604 1
 
< 0.1%
1591 2
< 0.1%
1560 1
 
< 0.1%
1551 1
 
< 0.1%
1549 2
< 0.1%
1548 4
< 0.1%
1547 2
< 0.1%

osrm_distance
Real number (ℝ)

High correlation 

Distinct18849
Distinct (%)98.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean260.53128
Minimum9.1019
Maximum2191.1664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:28.820090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.1019
5-th percentile12.58666
Q129.0895
median71.8738
Q3291.8383
95-th percentile1212.98
Maximum2191.1664
Range2182.0645
Interquartile range (IQR)262.7488

Descriptive statistics

Standard deviation400.16469
Coefficient of variation (CV)1.5359564
Kurtosis4.6476729
Mean260.53128
Median Absolute Deviation (MAD)51.182
Skewness2.223497
Sum4983702.9
Variance160131.78
MonotonicityNot monotonic
2024-10-30T11:17:29.541485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.8935 3
 
< 0.1%
48.7195 2
 
< 0.1%
10.7432 2
 
< 0.1%
175.319 2
 
< 0.1%
9.6941 2
 
< 0.1%
22.4752 2
 
< 0.1%
38.5491 2
 
< 0.1%
53.4168 2
 
< 0.1%
30.6035 2
 
< 0.1%
249.2718 2
 
< 0.1%
Other values (18839) 19108
99.9%
ValueCountFrequency (%)
9.1019 1
< 0.1%
9.1306 1
< 0.1%
9.1364 2
< 0.1%
9.1414 1
< 0.1%
9.202 1
< 0.1%
9.2256 1
< 0.1%
9.231 1
< 0.1%
9.2362 1
< 0.1%
9.24 1
< 0.1%
9.2441 1
< 0.1%
ValueCountFrequency (%)
2191.1664 1
< 0.1%
2189.8849 1
< 0.1%
2185.2996 1
< 0.1%
2185.1355 1
< 0.1%
2152.9283 1
< 0.1%
2152.903 1
< 0.1%
2129.7759 1
< 0.1%
2116.017 1
< 0.1%
2095.6729 1
< 0.1%
2091.6063 1
< 0.1%

factor
Real number (ℝ)

High correlation  Skewed 

Distinct9418
Distinct (%)49.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.0738659
Minimum0.25
Maximum77.387097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:29.857198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile1.1666667
Q11.5972851
median1.8528897
Q32.2068966
95-th percentile3.5454545
Maximum77.387097
Range77.137097
Interquartile range (IQR)0.60961148

Descriptive statistics

Standard deviation1.3699455
Coefficient of variation (CV)0.66057576
Kurtosis849.37163
Mean2.0738659
Median Absolute Deviation (MAD)0.29294367
Skewness20.412573
Sum39670.98
Variance1.8767508
MonotonicityNot monotonic
2024-10-30T11:17:30.174875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 337
 
1.8%
1.5 181
 
0.9%
1.666666667 116
 
0.6%
1.75 104
 
0.5%
3 81
 
0.4%
1.333333333 76
 
0.4%
1 76
 
0.4%
2.25 73
 
0.4%
1.8 68
 
0.4%
1.714285714 67
 
0.4%
Other values (9408) 17950
93.8%
ValueCountFrequency (%)
0.25 1
< 0.1%
0.2727272727 1
< 0.1%
0.2941176471 1
< 0.1%
0.3440860215 1
< 0.1%
0.3529411765 1
< 0.1%
0.3537414966 1
< 0.1%
0.3880597015 1
< 0.1%
0.4 1
< 0.1%
0.4153846154 1
< 0.1%
0.4342105263 1
< 0.1%
ValueCountFrequency (%)
77.38709677 1
< 0.1%
66.38888889 1
< 0.1%
45.6875 1
< 0.1%
33.70588235 1
< 0.1%
32.9375 1
< 0.1%
31.2 1
< 0.1%
26.31818182 1
< 0.1%
22.92592593 1
< 0.1%
22.8 1
< 0.1%
21.20408163 1
< 0.1%

segment_actual_time
Real number (ℝ)

High correlation  Zeros 

Distinct311
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.495426
Minimum-26
Maximum2297
Zeros253
Zeros (%)1.3%
Negative4
Negative (%)< 0.1%
Memory size149.6 KiB
2024-10-30T11:17:30.506022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-26
5-th percentile7
Q120
median29
Q340
95-th percentile76
Maximum2297
Range2323
Interquartile range (IQR)20

Descriptive statistics

Standard deviation46.257
Coefficient of variation (CV)1.303182
Kurtosis449.4569
Mean35.495426
Median Absolute Deviation (MAD)10
Skewness15.416725
Sum678992
Variance2139.7101
MonotonicityNot monotonic
2024-10-30T11:17:30.792782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 789
 
4.1%
26 680
 
3.6%
30 618
 
3.2%
28 581
 
3.0%
36 576
 
3.0%
32 572
 
3.0%
27 557
 
2.9%
23 514
 
2.7%
12 508
 
2.7%
25 469
 
2.5%
Other values (301) 13265
69.3%
ValueCountFrequency (%)
-26 1
 
< 0.1%
-21 1
 
< 0.1%
-5 1
 
< 0.1%
-1 1
 
< 0.1%
0 253
1.3%
1 46
 
0.2%
2 92
 
0.5%
3 74
 
0.4%
4 118
0.6%
5 119
0.6%
ValueCountFrequency (%)
2297 1
< 0.1%
1140 1
< 0.1%
1136 1
< 0.1%
1117 1
< 0.1%
1039 1
< 0.1%
1038 1
< 0.1%
990 1
< 0.1%
942 1
< 0.1%
901 1
< 0.1%
833 1
< 0.1%

segment_osrm_time
Real number (ℝ)

High correlation  Zeros 

Distinct126
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean18.404151
Minimum0
Maximum469
Zeros311
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:31.058296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111
median17
Q322
95-th percentile37
Maximum469
Range469
Interquartile range (IQR)11

Descriptive statistics

Standard deviation13.515165
Coefficient of variation (CV)0.73435418
Kurtosis150.74774
Mean18.404151
Median Absolute Deviation (MAD)6
Skewness7.0713552
Sum352053
Variance182.65969
MonotonicityNot monotonic
2024-10-30T11:17:31.356857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1462
 
7.6%
17 1349
 
7.1%
18 1198
 
6.3%
19 909
 
4.8%
15 829
 
4.3%
20 791
 
4.1%
21 673
 
3.5%
9 656
 
3.4%
22 652
 
3.4%
8 643
 
3.4%
Other values (116) 9967
52.1%
ValueCountFrequency (%)
0 311
1.6%
1 179
 
0.9%
2 185
 
1.0%
3 250
 
1.3%
4 280
1.5%
5 291
1.5%
6 530
2.8%
7 614
3.2%
8 643
3.4%
9 656
3.4%
ValueCountFrequency (%)
469 1
< 0.1%
407 1
< 0.1%
383 1
< 0.1%
234 1
< 0.1%
227 1
< 0.1%
221 1
< 0.1%
218 1
< 0.1%
184 1
< 0.1%
180 1
< 0.1%
174 1
< 0.1%

segment_osrm_distance
Real number (ℝ)

High correlation  Zeros 

Distinct18290
Distinct (%)95.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22.605794
Minimum0
Maximum635.1196
Zeros207
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size149.6 KiB
2024-10-30T11:17:31.631005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.16402
Q111.9519
median23.4432
Q327.6426
95-th percentile42.8949
Maximum635.1196
Range635.1196
Interquartile range (IQR)15.6907

Descriptive statistics

Standard deviation16.018336
Coefficient of variation (CV)0.70859428
Kurtosis221.125
Mean22.605794
Median Absolute Deviation (MAD)7.5607
Skewness8.4675763
Sum432426.23
Variance256.5871
MonotonicityNot monotonic
2024-10-30T11:17:31.922700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 207
 
1.1%
12.7007 3
 
< 0.1%
22.9515 3
 
< 0.1%
27.1815 3
 
< 0.1%
25.771 3
 
< 0.1%
25.189 3
 
< 0.1%
24.4452 3
 
< 0.1%
24.2347 3
 
< 0.1%
22.8617 3
 
< 0.1%
22.4401 3
 
< 0.1%
Other values (18280) 18895
98.8%
ValueCountFrequency (%)
0 207
1.1%
0.011 1
 
< 0.1%
0.0194 1
 
< 0.1%
0.0198 1
 
< 0.1%
0.0509 1
 
< 0.1%
0.0842 1
 
< 0.1%
0.0931 1
 
< 0.1%
0.1177 1
 
< 0.1%
0.138 1
 
< 0.1%
0.1478 1
 
< 0.1%
ValueCountFrequency (%)
635.1196 1
< 0.1%
541.9036 1
< 0.1%
439.6047 1
< 0.1%
320.7535 1
< 0.1%
284.7385 1
< 0.1%
265.0528 1
< 0.1%
249.4472 1
< 0.1%
227.9952 1
< 0.1%
222.5406 1
< 0.1%
216.3338 1
< 0.1%

segment_factor
Real number (ℝ)

High correlation  Skewed 

Distinct2146
Distinct (%)11.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.2227136
Minimum-1.8571429
Maximum574.25
Zeros22
Zeros (%)0.1%
Negative314
Negative (%)1.6%
Memory size149.6 KiB
2024-10-30T11:17:32.226012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.8571429
5-th percentile0.72727273
Q11.35
median1.6875
Q32.25
95-th percentile4.3333333
Maximum574.25
Range576.10714
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation7.4267373
Coefficient of variation (CV)3.3412929
Kurtosis4497.771
Mean2.2227136
Median Absolute Deviation (MAD)0.41477273
Skewness62.263468
Sum42518.289
Variance55.156427
MonotonicityNot monotonic
2024-10-30T11:17:32.519520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 815
 
4.3%
1.5 617
 
3.2%
1.333333333 324
 
1.7%
1 319
 
1.7%
-1 311
 
1.6%
1.666666667 304
 
1.6%
3 243
 
1.3%
1.75 233
 
1.2%
1.6 185
 
1.0%
2.5 176
 
0.9%
Other values (2136) 15602
81.6%
ValueCountFrequency (%)
-1.857142857 1
 
< 0.1%
-1 311
1.6%
-0.7 1
 
< 0.1%
-0.4545454545 1
 
< 0.1%
0 22
 
0.1%
0.03076923077 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06172839506 1
 
< 0.1%
0.06896551724 1
 
< 0.1%
0.07692307692 2
 
< 0.1%
ValueCountFrequency (%)
574.25 1
< 0.1%
558.5 1
< 0.1%
493 1
< 0.1%
145 1
< 0.1%
122.2 1
< 0.1%
107 1
< 0.1%
83.66666667 1
< 0.1%
76.33333333 1
< 0.1%
72 2
< 0.1%
62.5 1
< 0.1%

Interactions

2024-10-30T11:17:07.711827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:30.914773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:34.069816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:39.545468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:43.530088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:46.425530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:49.633448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:54.981942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:58.035904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:01.319611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:04.109892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:08.126631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:31.209033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:34.562717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:39.990366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:43.788773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:46.691180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:49.896641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:55.389968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:58.303166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:01.591508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:04.387028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:08.496613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:31.483896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:34.963311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:40.420954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:44.048193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:46.964496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:50.154654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:55.661880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:58.566888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:01.844638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:04.648252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:08.890410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:31.750838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:35.409863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:40.772793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:44.356920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:47.243587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:50.414189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:55.940482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:58.823225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:02.119555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:04.938048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:09.271983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:32.014398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:35.827413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:41.028039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:44.611581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:47.520462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:50.675678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:56.195390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:59.087957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:02.363412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:05.222438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:09.590140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:32.305497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:36.298755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:41.916019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:44.888094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:47.792370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:51.078829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:56.450996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:59.363364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:02.624505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:05.567917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:09.825132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:32.576235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:36.727160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:42.191270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:45.125959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:48.287490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:51.429285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:56.701527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:59.609379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:02.855313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:05.935071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:10.090811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:32.858680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:37.202854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:42.468058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:45.419387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:48.572599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:52.494078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:56.978507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:59.873450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:03.131542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:06.334221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:10.332088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:33.123629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:37.682451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:42.717119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:45.672770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:48.820139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:53.019065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:57.249463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:00.550606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:03.374867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:06.687437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:10.588518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:33.399127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:38.115068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:42.968997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:45.906838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:49.075639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:53.738007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:57.489884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:00.781379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:03.604706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:07.029933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:10.832985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:33.711498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:38.585852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:43.273792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:46.164770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:49.343197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:54.557773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:16:57.754919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:01.062224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:03.847919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-30T11:17:07.387503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-30T11:17:32.782631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
actual_distance_to_destinationactual_timecutoff_factordatafactoris_cutoffosrm_distanceosrm_timeroute_typesegment_actual_timesegment_factorsegment_osrm_distancesegment_osrm_timestart_scan_to_end_scan
actual_distance_to_destination1.0000.9490.9980.0980.0260.2200.9900.9780.4280.277-0.0740.4940.3780.816
actual_time0.9491.0000.9500.1010.2350.1960.9570.9540.4220.3620.0490.4620.3590.821
cutoff_factor0.9980.9501.0000.0980.0290.2190.9900.9780.4280.273-0.0720.4860.3720.815
data0.0980.1010.0981.0000.0000.0160.0990.0980.0340.0070.0000.0100.0000.233
factor0.0260.2350.0290.0001.0000.0730.021-0.0210.0400.2850.572-0.062-0.1400.087
is_cutoff0.2200.1960.2190.0160.0731.0000.2200.2190.1440.0790.0440.0000.0000.328
osrm_distance0.9900.9570.9900.0990.0210.2201.0000.9910.4250.293-0.0810.5140.4030.819
osrm_time0.9780.9540.9780.098-0.0210.2190.9911.0000.4350.285-0.1140.5010.4170.812
route_type0.4280.4220.4280.0340.0400.1440.4250.4351.0000.0200.0150.0570.0790.563
segment_actual_time0.2770.3620.2730.0070.2850.0790.2930.2850.0201.0000.4590.6780.6660.351
segment_factor-0.0740.049-0.0720.0000.5720.044-0.081-0.1140.0150.4591.000-0.132-0.227-0.037
segment_osrm_distance0.4940.4620.4860.010-0.0620.0000.5140.5010.0570.678-0.1321.0000.9230.530
segment_osrm_time0.3780.3590.3720.000-0.1400.0000.4030.4170.0790.666-0.2270.9231.0000.423
start_scan_to_end_scan0.8160.8210.8150.2330.0870.3280.8190.8120.5630.351-0.0370.5300.4231.000

Missing values

2024-10-30T11:17:11.304308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-30T11:17:12.112559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-30T11:17:13.256634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datatrip_creation_timeroute_schedule_uuidroute_typetrip_uuidsource_centersource_namedestination_centerdestination_nameod_start_timeod_end_timestart_scan_to_end_scanis_cutoffcutoff_factorcutoff_timestampactual_distance_to_destinationactual_timeosrm_timeosrm_distancefactorsegment_actual_timesegment_osrm_timesegment_osrm_distancesegment_factor
0training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388121AAAAnand_VUNagar_DC (Gujarat)IND388620AABKhambhat_MotvdDPP_D (Gujarat)2018-09-20 03:21:32.4186002018-09-20 04:47:45.23679786.0True9.02018-09-20 04:27:5510.43566014.011.011.96531.27272714.011.011.96531.272727
1training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388121AAAAnand_VUNagar_DC (Gujarat)IND388620AABKhambhat_MotvdDPP_D (Gujarat)2018-09-20 03:21:32.4186002018-09-20 04:47:45.23679786.0True18.02018-09-20 04:17:5518.93684224.020.021.72431.20000010.09.09.75901.111111
2training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388121AAAAnand_VUNagar_DC (Gujarat)IND388620AABKhambhat_MotvdDPP_D (Gujarat)2018-09-20 03:21:32.4186002018-09-20 04:47:45.23679786.0True27.02018-09-20 04:01:19.50558627.63727940.028.032.53951.42857116.07.010.81522.285714
3training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388121AAAAnand_VUNagar_DC (Gujarat)IND388620AABKhambhat_MotvdDPP_D (Gujarat)2018-09-20 03:21:32.4186002018-09-20 04:47:45.23679786.0True36.02018-09-20 03:39:5736.11802862.040.045.56201.55000021.012.013.02241.750000
4training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388121AAAAnand_VUNagar_DC (Gujarat)IND388620AABKhambhat_MotvdDPP_D (Gujarat)2018-09-20 03:21:32.4186002018-09-20 04:47:45.23679786.0False39.02018-09-20 03:33:5539.38604068.044.054.21811.5454556.05.03.91531.200000
5training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388620AABKhambhat_MotvdDPP_D (Gujarat)IND388320AAAAnand_Vaghasi_IP (Gujarat)2018-09-20 04:47:45.2367972018-09-20 06:36:55.627764109.0True9.02018-09-20 06:15:5810.40303815.011.012.11711.36363615.011.012.11711.363636
6training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388620AABKhambhat_MotvdDPP_D (Gujarat)IND388320AAAAnand_Vaghasi_IP (Gujarat)2018-09-20 04:47:45.2367972018-09-20 06:36:55.627764109.0True18.02018-09-20 05:47:2918.04548144.017.021.28902.58823528.06.09.17194.666667
7training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388620AABKhambhat_MotvdDPP_D (Gujarat)IND388320AAAAnand_Vaghasi_IP (Gujarat)2018-09-20 04:47:45.2367972018-09-20 06:36:55.627764109.0True27.02018-09-20 05:25:5828.06189665.029.035.82522.24137921.011.014.53621.909091
8training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388620AABKhambhat_MotvdDPP_D (Gujarat)IND388320AAAAnand_Vaghasi_IP (Gujarat)2018-09-20 04:47:45.2367972018-09-20 06:36:55.627764109.0True36.02018-09-20 05:15:5638.93916776.039.047.19001.94871810.010.011.36481.000000
9training2018-09-20 02:35:36.476840thanos::sroute:eb7bfc78-b351-4c0e-a951-fa3d5c3297efCartingtrip-153741093647649320IND388620AABKhambhat_MotvdDPP_D (Gujarat)IND388320AAAAnand_Vaghasi_IP (Gujarat)2018-09-20 04:47:45.2367972018-09-20 06:36:55.627764109.0False43.02018-09-20 04:49:2043.595802102.045.053.23342.26666726.06.06.04344.333333
datatrip_creation_timeroute_schedule_uuidroute_typetrip_uuidsource_centersource_namedestination_centerdestination_nameod_start_timeod_end_timestart_scan_to_end_scanis_cutoffcutoff_factorcutoff_timestampactual_distance_to_destinationactual_timeosrm_timeosrm_distancefactorsegment_actual_timesegment_osrm_timesegment_osrm_distancesegment_factor
19120test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND842003AABJabalpur_Adhartal_IP (Madhya Pradesh)IND487001AABNarsinghpur_KndliDPP_D (Madhya Pradesh)2018-10-03 21:30:56.5184602018-10-04 01:35:20.805767244.0True22.02018-10-04 00:56:1423.31825030.025.026.93801.20000030.025.026.93801.200000
19121test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND842003AABJabalpur_Adhartal_IP (Madhya Pradesh)IND487001AABNarsinghpur_KndliDPP_D (Madhya Pradesh)2018-10-03 21:30:56.5184602018-10-04 01:35:20.805767244.0True44.02018-10-04 00:20:1744.97396465.047.050.58321.38297935.021.023.64521.666667
19122test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND842003AABJabalpur_Adhartal_IP (Madhya Pradesh)IND487001AABNarsinghpur_KndliDPP_D (Madhya Pradesh)2018-10-03 21:30:56.5184602018-10-04 01:35:20.805767244.0True66.02018-10-03 22:30:1773.369213175.074.086.58532.364865110.027.036.00214.074074
19123test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND842003AABJabalpur_Adhartal_IP (Madhya Pradesh)IND487001AABNarsinghpur_KndliDPP_D (Madhya Pradesh)2018-10-03 21:30:56.5184602018-10-04 01:35:20.805767244.0False84.02018-10-03 22:12:1384.479723194.080.094.62732.42500018.08.012.40322.250000
19124test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND487001AABNarsinghpur_KndliDPP_D (Madhya Pradesh)IND487551AAAGadarwara_MPward_D (Madhya Pradesh)2018-10-04 01:57:29.9648682018-10-04 03:16:19.55053878.0True22.02018-10-04 02:38:1723.14292938.027.028.70711.40740738.027.028.70711.407407
19125test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND487001AABNarsinghpur_KndliDPP_D (Madhya Pradesh)IND487551AAAGadarwara_MPward_D (Madhya Pradesh)2018-10-04 01:57:29.9648682018-10-04 03:16:19.55053878.0False41.02018-10-04 02:12:1541.71330164.048.052.36471.33333326.020.023.65761.300000
19126test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND487551AAAGadarwara_MPward_D (Madhya Pradesh)IND464668AAABareli_SourvDPP_D (Madhya Pradesh)2018-10-04 03:16:19.5505382018-10-04 06:25:37.976768189.0True22.02018-10-04 05:22:2222.40859548.034.031.57661.41176548.034.031.57661.411765
19127test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND487551AAAGadarwara_MPward_D (Madhya Pradesh)IND464668AAABareli_SourvDPP_D (Madhya Pradesh)2018-10-04 03:16:19.5505382018-10-04 06:25:37.976768189.0True44.02018-10-04 04:02:1944.195324128.067.080.99431.91044880.039.041.41022.051282
19128test2018-10-03 21:30:56.518460thanos::sroute:0ac760f3-96cb-4046-bfd0-8bc467805780FTLtrip-153860225651817784IND487551AAAGadarwara_MPward_D (Madhya Pradesh)IND464668AAABareli_SourvDPP_D (Madhya Pradesh)2018-10-04 03:16:19.5505382018-10-04 06:25:37.976768189.0False57.02018-10-04 03:42:1457.341503148.062.075.00912.38709720.018.018.28761.111111
19129training2018-09-21 06:43:44.036195thanos::sroute:9f229200-bc86-4418-90ae-653498358781Cartingtrip-153751222403597152IND141003AABLudhianaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN